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Natural Language Processing with Java

Natural Language Processing with Java

By : Richard M. Reese
2 (3)
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Natural Language Processing with Java

Natural Language Processing with Java

2 (3)
By: Richard M. Reese

Overview of this book

Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes. You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more. By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.
Table of Contents (14 chapters)
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Extracting relationships


There are a number of techniques available to extract relationships. These can be grouped as follows:

  • Hand-built patterns
  • Supervised methods
  • Semi-supervised or unsupervised methods
  • Bootstrapping methods
  • Distant supervision methods
  • Unsupervised methods

Hand-built models are used when we have no training data. This can occur with new business domains or entirely new types of projects. These often require the use of rules. A rule might be:

"If the word "actor" or "actress" is used and not the word "movie" or "commercial", then the text should be classified as a play."

However, this approach takes a lot of effort and needs to be adjusted for the actual text in-hand.

If only a little training data is amiable, then the Naive Bayes classifier is a good choice. When more data is available, then techniques such as support vector machine (SVM), regularized logistic regression, and random forest can be used.

Although it is useful to understand these techniques in more detail, we will...

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